Sequential Monte Carlo on large binary sampling spaces

Statistics and Computing

Volumn 23, Issue 2, 2013, Pages 163-184

  Schäfer, Christian ^a,b   Chopin, Nicolas ^a,c  

^a CREST   (France)

^b UNIVERSITÉ PARIS DAUPHINE   (France)

^c Ecole Nationale de la Statistique et de l'Administration   (France)

Author keywords

Adaptive Monte Carlo; Linear regression; Multivariate binary data; Sequential Monte Carlo; Variable selection

Indexed keywords

EID: 84872617354     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-011-9299-z     Document Type: Review

References (46)

1. Albert, A., Anderson, J. A.: On the existence of maximum likelihood estimates in logistic regression models. Biometrika 72, 1-10 (1984).
2. Andrieu, C., Thoms, J.: A tutorial on adaptive MCMC. Stat. Comput. 18(4), 343-373 (2008).
3. Bahadur, R.: A representation of the joint distribution of responses to n dichotomous items. In: Solomon, H. (ed.) Studies in Item Analysis and Prediction, pp. 158-168. Stanford University Press, Stanford (1961).
4. Bottolo, L., Richardson, S.: Evolutionary stochastic search for Bayesian model exploration. Bayesian Anal. 5(3), 583-618 (2010).
5. Cappé, O., Douc, R., Guillin, A., Marin, J., Robert, C.: Adaptive importance sampling in general mixture classes. Stat. Comput. 18(4), 447-459 (2008).
6. Carpenter, J., Clifford, P., Fearnhead, P.: Improved Particle Filter for nonlinear problems. IEE Proc. Radar Sonar Navig. 146(1), 2-7 (1999).
7. Chopin, N.: A sequential particle filter method for static models. Biometrika 89(3), 539 (2002).
8. Clyde, M., Parmigiani, G.: Protein construct storage: Bayesian variable selection and prediction with mixtures. J. Biopharm. Stat. 8(3), 431 (1998).
9. Clyde, M., Ghosh, J., Littman, M.: Bayesian adaptive sampling for variable selection and model averaging. J. Comput. Graph. Stat. 20(1), 80-101 (2011).
10. Cox, D.: The analysis of multivariate binary data. Appl. Stat. 113-120 (1972).
11. Cox, D., Wermuth, N.: A note on the quadratic exponential binary distribution. Biometrika 81(2), 403-408 (1994).
12. Cox, D., Wermuth, N.: On some models for multivariate binary variables parallel in complexity with the multivariate Gaussian distribution. Biometrika 89(2), 462 (2002).
13. Del Moral, P., Doucet, A., Jasra, A.: Sequential Monte Carlo samplers. J. R. Stat. Soc., Ser. B, Stat. Methodol. 68(3), 411-436 (2006).
14. Dongarra, J., Moler, C., Bunch, J., Stewart, G.: LINPACK: Users' Guide. Society for Industrial and Applied Mathematics, Philadelphia (1979).
15. Emrich, L., Piedmonte, M.: A method for generating high dimensional multivariate binary variates. Am. Stat. 45, 302-304 (1991).
16. Fearnhead, P., Clifford, P.: Online inference for hidden Markov models via particle filters. J. R. Stat. Soc., Ser. B, Stat. Methodol. 65(4), 887-899 (2003).
17. Firth, D.: Bias reduction of maximum likelihood estimates. Biometrika 80, 27-38 (1993).
18. Gelman, A., Meng, X.: Simulating normalizing constants: From importance sampling to bridge sampling to path sampling. Stat. Sci. 13(2), 163-185 (1998).
19. Genest, C., Neslehova, J.: A primer on copulas for count data. ASTIN Bull. 37(2), 475 (2007).
20. George, E. I., McCulloch, R. E.: Approaches for Bayesian variable selection. Stat. Sin. 7, 339-373 (1997).
21. Gilks, W., Berzuini, C.: Following a moving target Monte Carlo inference for dynamic Bayesian models. J. R. Stat. Soc., Ser. B, Stat. Methodol. 63(1), 127-146 (2001).
22. Gordon, N. J., Salmond, D. J., Smith, A. F. M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. Radar Sonar Navig. 140(2), 107-113 (1993).
23. Harrison, D., Rubinfeld, D. L.: Hedonic housing prices and the demand for clean air. J. Environ. Econ. Manag. 5(1), 81-102 (1978).
24. Jasra, A., Stephens, D., Doucet, A., Tsagaris, T.: Inference for Lévy-Driven stochastic volatility models via adaptive sequential Monte Carlo. Scand. J. Stat. (2008).
25. Joe, H.: Families of m-variate distributions with given margins and m (m-1)/2 bivariate dependence parameters. Lect. Notes Monogr. Ser. 28, 120-141 (1996).
26. Kitagawa, G.: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Stat. 5(1), 1-25 (1996).
27. Kong, A., Liu, J. S., Wong, W. H.: Sequential imputation and Bayesian missing data problems. J. Am. Stat. Assoc. 89, 278-288 (1994).
28. Lee, A.: Generating random binary deviates having fixed marginal distributions and specified degrees of association. Am. Stat. 47(3) (1993).
29. Lee, A., Yau, C., Giles, M., Doucet, A., Holmes, C.: On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. J. Comput. Graph. Stat. 19(4), 769-789 (2010).
30. Leisch, F., Weingessel, A., Hornik, K.: On the generation of correlated artificial binary data. Technical report, WU Vienna University of Economics and Business (1998).
31. Liang, F., Wong, W.: Evolutionary Monte Carlo: Applications to Cp model sampling and change point problem. Stat. Sin. 10(2), 317-342 (2000).
32. Liu, J.: Peskun's theorem and a modified discrete-state Gibbs sampler. Biometrika 83(3), 681-682 (1996).
33. Liu, J., Chen, R.: Sequential Monte Carlo methods for dynamic systems. J. Am. Stat. Assoc. 93(443), 1032-1044 (1998).
34. Lunn, A., Davies, S.: A note on generating correlated binary variables. Biometrika 85(2), 487-490 (1998).
35. Neal, R.: Annealed importance sampling. Stat. Comput. 11(2), 125-139 (2001).
36. Nelsen, R.: An Introduction to Copulas. Springer, Berlin (2006).
37. Nott, D., Kohn, R.: Adaptive sampling for Bayesian variable selection. Biometrika 92(4), 747 (2005).
38. Oman, S., Zucker, D.: Modelling and generating correlated binary variables. Biometrika 88(1), 287 (2001).
39. Park, C., Park, T., Shin, D.: A simple method for generating correlated binary variates. Am. Stat. 50(4) (1996).
40. Qaqish, B.: A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means and correlations. Biometrika 90(2), 455 (2003).
41. Robert, C., Casella, G.: Monte Carlo Statistical Methods. Springer, Berlin (2004).
42. Schäfer, C.: Parametric families on large binary spaces. Technical report, Centre de Recherche en Economie et en Statistique, Paris (2011).
43. Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6, 461-464 (1978).
44. Suchard, M., Holmes, C., West, M.: Some of the what?, why?, how?, who? and where? of graphics processing unit computing for Bayesian analysis. In: Bernardo, J. M. (ed.) Bayesian Statistics, vol. 9. Oxford University Press, London (2010).
45. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc., Ser. B, Methodol. 58(1), 267-288 (1996).
46. Yeh, I.: Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797-1808 (1998).